As you recognize the need for implementing ML, we study your tasks, assume the solution, and plan the scope of work and development process.

During this lengthy but critical step, we analyze your data, visualize it for better understanding, potentially select a subset of the most useful data, and then preprocess and transform it to create a legitimate dataset. After that, we split the dataset into three sets of data: training, (cross)validation, and test sets. The first - to train a model and define its parameters. The second - to tweak the model's settings and parameters to achieve the best results. And the third - to evaluate a real model's performance to solve a task after training.

The project continues even after the model is completed. We will help you track the metrics and apply testing to define your model's performance over time and improve it when needed.

After cleaning data and subtracting from it, we start adding to it in an essential data preparation process - feature engineering. The key element of spot-on model accuracy, feature engineering is about using domain knowledge to manually create new features in a raw dataset. This requires a deep understanding of a specific industry and the problem the model will help solve.

The process of putting a model into production depends on your business infrastructure, the volume of data, the accuracy of all previous stages, and whether you're using machine learning as a service product.

Here we will train a few models to decide which one gives the most accurate results. We experiment with many different types of models, feature selection, regularization and hyperparameters tuning until we get a well-trained model - neither underfit or overfit. For each experiment, we evaluate model accuracy using the appropriate metric for exactly this type of problem and dataset.